October 14, 2026 · By DataTecnica

AI Variant Interpretation: ClinVar, gnomAD and the Literature in One Workflow

See how AI helps gather variant evidence from ClinVar, gnomAD, Ensembl VEP and the literature into one cited table, and what still needs expert review.

Interpreting a genetic variant means gathering evidence from several places and weighing it. Is the variant already classified? How common is it in the population? What does it do to the transcript and protein? What has been published about it? The slow part is not the weighing. It is the gathering, across databases that each have their own interface, identifiers and quirks.

AI variant interpretation, as described here, automates the gathering and presents the evidence together, with sources. It does not replace the person who classifies the variant.

The four questions every variant review starts with

Question	Where the answer lives
Has anyone already classified it, and for what condition?	ClinVar
How common is it, and how tolerant is the gene to this kind of change?	gnomAD allele frequencies and gene constraint
What is its predicted effect on the gene and protein?	Ensembl, including VEP consequences on GRCh38 and GRCh37
What has been published about it?	PubMed (through PubTator) and a curated variant-publication database

In codon.bio a single question spans all four. Ask about a variant, and the agents query each source in parallel and return an evidence table with citations attached.

What each source tells you

ClinVar. The submitted classifications, the conditions they relate to, and the review status behind them. Conflicting classifications from different submitters are common and worth reading closely, not averaging.

gnomAD. Population allele frequencies and allele counts from gnomAD v4 (GRCh38), plus gene-level constraint metrics and in silico predictions. A variant that is too common in a healthy population for the disease in question is a very different proposition from an absent one. Frequency is also population-specific, so look at the ancestry breakdown, not only the overall number.

Ensembl and VEP. Consequences against the right transcript and assembly. Annotating a variant against the wrong genome build or the wrong transcript is a classic source of error, so state the build explicitly. codon.bio supports both GRCh38 and GRCh37.

Association data. For a variant tied to neurodegeneration, full GWAS summary statistics for Alzheimer's, Parkinson's, ALS, FTD and Lewy body dementia show its association signal directly, not only the lead hits a paper reported.

The literature. Case reports, functional studies and cohort data. This is often where the decisive evidence is, and also where the variant is written about under several nomenclatures, so a plain keyword search misses papers.

A workflow

State the variant and the build. Use a clear identifier and say GRCh38 or GRCh37.
Ask for the evidence table. Request ClinVar classification, gnomAD frequency and constraint, VEP consequence, and relevant publications for the variant.
Read the conflicts. Where sources disagree, the disagreement is the finding. Note it instead of resolving it by default.
Check the papers behind the claims that matter. Open the cited publications for any evidence you intend to rely on.
Record the reasoning. Keep the question, the sources and your conclusion in one project so the work can be reviewed or extended later.
Make the call yourself. Classification is a judgement made by a qualified reviewer, following the framework your lab or organisation uses.

What it is and is not

It gathers and organises evidence from named databases and the literature, and keeps the citations.
It does not classify variants. It does not apply a classification framework for you, and it is not a clinical diagnostic tool. Clinical decisions require qualified review.
It is only as current as its sources. Databases are updated on their own schedules, so confirm anything critical in the source itself.
It is built not to guess. The agents are instructed to answer and cite only from what the sources returned, and to say so when they found nothing. That is why each claim can be traced back to a source, and why you should still check the ones that matter.

For researchers and for teams

Researchers use this to triage lists of candidate variants and to prepare for a closer review. Teams use shared projects so that a variant's evidence, the discussion about it and the final reasoning stay together. If you work with variant files, you can upload VCFs and other data to a project and analyse them in a real Python and R environment alongside the database evidence.

Questions people ask

Can AI interpret a genetic variant?

It can gather and present the evidence quickly and with citations. Interpreting the variant, meaning deciding what it means for a patient or a study, remains a task for a qualified person.

Which databases does it use for variants?

ClinVar, gnomAD, Ensembl (including VEP), NCBI Gene and dbSNP, UniProtKB/Swiss-Prot, the GWAS Catalogue and full GWAS summary statistics for Alzheimer's, Parkinson's, ALS, FTD and Lewy body dementia, and the literature through PubMed (via PubTator), Semantic Scholar and a curated variant-publication database.

Does it support GRCh37 and GRCh38?

Yes for Ensembl: VEP consequences are available on both builds. gnomAD data comes from gnomAD v4, which is on GRCh38.

Is this a clinical tool?

No. It supports research and evidence gathering. Do not use it as a substitute for clinical-grade interpretation.

See it on a variant you know

Take a variant whose classification you already trust. Ask codon.bio for its evidence and compare the table to what you expect. That is the fastest way to see what the workflow adds and where you would still check by hand.